From: Tim Hochberg <tim.hochberg@ie...>  20030204 16:51:39

I was inspired by Armin's latest Psyco version to try and see how well one could do with NumPy/NumArray implemented in Psycotic Python. I wrote a bare bones, pure Python, Numeric array class loosely based on Jnumeric (which in turn was loosely based on Numeric). The buffer is just Python's array.array. At the moment, all that one can do to the arrays is add and index them and the code is still a bit of a mess. I plan to clean things up over the next week in my copius free time <0.999 wink> and at that point it should be easy add the remaining operations. I benchmarked this code, which I'm calling Psymeric for the moment, against NumPy and Numarray to see how it did. I used a variety of array sizes, but mostly relatively large arrays of shape (500,100) and of type Float64 and Int32 (mixed and with consistent types) as well as scalar values. Looking at the benchmark data one comes to three main conclusions: * For small arrays NumPy always wins. Both Numarray and Psymeric have much larger overhead. * For large, contiguouse arrays, Numarray is about twice as fast as either of the other two. * For large, noncontiguous arrays, Psymeric and NumPy are ~20% faster than Numarray The impressive thing is that Psymeric is generally slightly faster than NumPy when adding two arrays. It's slightly slower (~10%) when adding an array and a scalar although I suspect that could be fixed by some special casing a la Numarray. Adding two (500,100) arrays of type Float64 together results in the following timings: psymeric numpy numarray contiguous 0.0034 s 0.0038 s 0.0019 s stride2 0.0020 s 0.0023 s 0.0033 s I'm not sure if this is important, but it is an impressive demonstration of Psyco! More later when I get the code a bit more cleaned up. tim 0.002355 0.002355 